Remote Monitoring of Patients in Clinical Trials

Recent initiatives to redesign the clinical trial process have in part focused on the creation of trials that are more patient focused. Two significant enabling factors are investment in mobile health (mHealth) by global health services and the increasing diversity and adoption of consumer and medical grade wearables and sensors. The pursuit of mHealth solutions has been driven by an aging population and increased incidence of chronic diseases, presenting healthcare systems globally with an almost insurmountable budgetary challenge. National and international bodies are examining ways of creating system efficiencies so that national health systems can provide medical care in a more cost-effective and innovative way. By contributing to a more patient-focused, primary setting healthcare model, mHealth can support a shift towards prevention and at the same time improve the efficiency of the system generally.¹

The parallel explosion in the number of wearable sensors that can measure critical physiological indicators such as activity levels, sleep, blood pressure, heart rate, EEG and ECG offers many possibilities. In particular, the development of medical grade devices and sensors by companies such as iHealth, Withings and Alice Cor and the creation of digital health platforms by companies such as Validic and Qualcomm, have created an ecosystem that has the potential to facilitate the remote monitoring of patients outside the traditional clinical site.

The shift of clinical assessment from the controlled environment of a research site to the uncontrolled environment of a patient's home is a considerable challenge. A number of issues will determine the value of wearables and sensors in the clinical research setting: the composition of the selected sensor suite; relevance of the data generated to the disease, its treatment and the defined assessment endpoints; impact of the sensor suite on patient burden; transfer of the data from the home in a secure and timely fashion, conforming with local regulations and not bounded by signal strength or idiosyncratic mobile networks; and last, but not least, the integration of the data into a clinical data management system (CDMS) and reporting solution. The realization of the full value of the current nature of this type of data has not been given full consideration. We now have the opportunity to assess protocol and GCP compliance in a clinical trial in real time, at both the site and patient levels. 

Sensor Suite Approach

A suite of wearables and sensors can be combined and deployed in a non-clinical home setting, tailored to specific therapeutic areas (Figure 1).

The 2015 mHeatlh Grand Tour was an example of a disruptive innovation in terms of remote monitoring, of which ICON took part.  This observational trial used a remote monitoring approach to follow a group of cyclists, some of whom had Type 1 diabetes. Diabetes management has made significant strides since the first portable blood glucose meter was introduced in 1969. Live remote tracking of continuous glucose monitor (CGM) data became a reality in 2013.² During the 2015 mHealth Grand Tour, the riders were weighed daily and their CGM, activity, sleep and heart rate data were tracked remotely.

We wanted to take this concept further and develop a methodology to facilitate the real-time collection of data from a sensor suite, the transmission of the data in a secure and timely fashion and the integration of the data into a compliance and monitoring dashboard. The collection of real-time data in a home setting can give sponsors and trial managers greater visibility and assurance around GCP and protocol compliance in ways that were not previously viable. We believe that the real-time tracking of sensor suite data through compliance dashboards can be a key enabler for the wider adoption of remote wearables and sensors in clinical trials. 

Data Flow

We deployed commercially available devices and sensors to a number of healthy volunteers located in non-clinical settings in three different countries. We used FDA-cleared and consumer devices to generate physiological data similar to that used routinely in clinical trials: heart rate, activity/sleep, oximetry, weight and blood glucose. The data were integrated into an informatics hub via digital health platform for central analysis alongside other user attributes. We used a unique security token provided by the digital health platform to maintain a de-identified flow of data.

All users successfully used the unique security token provided to them to connect to an online data marketplace maintained by the digital health platform provider, allowing them to authorize the transfer of their data from the device vendor to the digital health platform. We used the vendor's application programming interface (API) to pull data directly from the digital health platform into our own informatics hub. The time lag between data collection by the remote device and integration into our informatics hub was consistently less than 30 seconds. Figure 2 summarizes the data flow.

We created some demonstration compliance dashboards to show how the integrated data could be analyzed routinely in the context of risk-based monitoring, safety monitoring, protocol compliance and subject engagement (Figure 3).

The dashboards display red/amber/green (RAG) icons for wear time, activity, sleep and heart rate, based on configurable compliance or key risk indicator rules, at both subject and site levels. They also display a weighted, balanced scorecard RAG icon across all categories, again based on configurable rules. We augmented the dashboard with maps to provide geographical context of overall compliance risk at the site level. 

The value of this approach in a clinical trial is the ability to track in real time or near real time parameters that could impact patient safety, the quality and variability of the data being generated and patient engagement. For example, in a pain study where the subjects have arthritis, sleep hygiene and activity levels can impact pain response. Similarly, pain levels can impact sleep and activity levels. Capturing and monitoring patients' sleep hygiene and activity levels and displaying this data in a dashboard can assist with the identification of patients who are not adhering to the protocol and engaging in behaviors that could introduce pain responses that are not reflective of the efficacy of the drug being investigated. It is self-evident that ensuring that subjects wear the devices and sensors for the period of time required to generate a valid data set is a key success factor. Dashboards that track wear and non-wear can support early intervention and engagement, optimizing the utility of mHealth technologies. 

Conclusions

To obtain all the outcome measures required by a protocol, multiple devices might be needed, and we have implemented a framework to securely collect clinically relevant data from devices from multiple vendors into an informatics hub. This approach allows us to measure clinical outcomes objectively and remotely from patients in non-clinical settings going about their daily lives.

Data collection without integration has a limited usefulness, so it was important to show how de-identified data collected through mobile devices can be re-integrated with the full set of clinical data on the patient in a secure environment in real time. Data collected in this way are more objective and have a more transparent audit trail than more traditional clinical outcome assessment instruments. We think there is huge potential in this approach to transform the way we monitor clinical trials, improving the quality of patient data, reducing patient burden, increasing patient engagement and redesigning the thinking around how we conduct clinical trials. This approach also allows us to easily compare sensor suite data with electronic patient-reported outcome data to provide further valuable insight into and cross-validation of remotely captured patient outcomes. 

The use of wearable technology in clinical trials has the potential to be one of the most disruptive innovations in drug development. A wealth of medical grade physiological data are being generated by wearable sensors, with the potential to create a new future where patients no longer have to visit research sites and where real-time data are available to allow for timely decisions to be made regarding patient safety and compliance, and even drug efficacy in adaptive trials. We know it is not that simple and that device selection, battery life, wear position and endpoint selection all impact the quality and value of the date being generated. However, we believe that the real-time tracking of remotely captured data is a key enabler. 

The use of dashboards to monitor GCP and protocol compliance on a daily or weekly basis gives trial monitors the ability to closely follow key compliance indicators and provides an element of control over the “uncontrolled” home environment where these device are being used. Such dashboards can also facilitate safety monitoring of key physiological markers such as heart rate, blood pressure, oximetry and weight, all of which can be assessed remotely against appropriate safety signals. The manner in which both compliance and safety monitoring flags are used and actioned needs to be further assessed and developed. 

The remote monitoring of patients using a suite of wearables and sensors has significant application in creating more patient-centric clinical trials, that the need for a patient to travel long distances to attend site visits can be replaced by a “virtual site visit" in the patient's home and that clinically relevant data that track both data quality and the safety and efficacy of drugs can be collected and transmitted remotely. 

Marie McCarthy is Director of Product Innovation Information Technology. Michael Philips is Director, Product Innovation. Bill Byrom is Senior Director, Product Innovation. Willie Muehlhausen is Vice President, Head of Innovation. All with ICON Clinical Research.

References

1. EU GREEN PAPER on mobile Health ("mHealth") 2014. Http://ec.europa.eu/digital-agenda/en/news/green-paper-mobile-health-mhealth.
2. mHealth Grand Tour 2015 Diabetes and Technology. http://www.mhealthtour.com/2015/diabetes-technology#section-159.